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Traducción e Interpretación

BITRA. BIBLIOGRAFÍA DE INTERPRETACIÓN Y TRADUCCIÓN

 
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Tema:   Automática.
Autor:   Zamora Martínez, Francisco Julián
Año:   2012
Título:   Aportaciones al modelado conexionista de lenguaje y su aplicación al reconocimiento de secuencias y traducción automática [Contributions to the connectionist model of language and its application to the recognition of sequences and to machine translation]
Lugar:   Valencia
https://riunet.upv.es/handle/10251/18066
Editorial/Revista:   Universitat Politécnica de València
Páginas:   317
Idioma:   Español.
Tipo:   Tesis.
Disponibilidad:   Acceso abierto.
Índice:   1. Aportaciones al modelo conexionista del lenguaje; 2. Aplicación de NN LMs al reconocimiento de secuencias; 3. Aplicación de NN LMs a la traducción automática estadística.
Resumen:   Natural Language Processing is an area of Artificial Intelligence, in particular, of Pattern Recognition. It is a multidisciplinary field that studies human language, both oral and written. It deals with the development and research of computational mechanisms for communication between people and computers, using natural languages. Natural Language Processing is a reasearch area constantly evolving, and this work focuses only on the part related to language modeling, and its application to various tasks: recognition/understanding of sequences and statistical machine translation.
Specifically, this thesis focus its interest on the so-called connectionist language models (or continuous space language models), i.e., language models based on neural networks. Their excellent performance in various Natural Language Processing areas has motivated this study.
[...] All developed algorithms were implemented in C++ and using Lua as scripting language. The implementations are compared with those that are considered standard on each of the addressed tasks. Neural network language models achieve very interesting improvements of quality over the reference baseline systems:
- competitive results are achieved on automatic speech recognition and spoken language understanding;
- improvement of state-of-the-art handwritten text recognition;
- state-of-the-art results on statistical machine translation, as was stated with the participation on international evaluation campaigns.
On sequence recognition tasks, the integration of neural network language models on the first decoding stage achieve very competitive computational costs. However, their integration in machine translation tasks requires a deeper development because the computation cost of the system is still somewhat high. [Source: Author]
Agradecimientos:   Record supplied by the Departament de Traducció i Interpretació i Estudis de l'Àsia Oriental (Universitat Autònoma de Barcelona).
 
 
2001-2021 Universidad de Alicante DOI: 10.14198/bitra
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